Real time monitoring and control processes is becoming increasingly important in the manufacture of semiconductor integrated circuits (ICs). This trend is driven by the escalating number and complexity of process steps used during IC fabrication. As the number of process steps increases, the cost of mis-processing a wafer, likewise, also increases, demanding the use of effective diagnostic methods for prompt detection and identification of process problems.
The tools required to achieve precise monitoring routinely necessitate calibrating the most essential and crucial components, such as mass flow controllers, chamber pressure manometers, rf-power, chamber leak-up rate, and the like. The internal status of a plasma process is checked periodically (e.g., on a daily basis) by measuring etch rates on blanket wafers. Furthermore, etch rates and uniformity depend on a tool performing mechanically and chemically in the same internal plasma state as when the tool was initially qualified.
Many of today's processes, including Reactive-Ion-Etch (RIE) as well as other plasma-related processes, make use of sophisticated sensors such as Optical Emission Spectroscopy (OES) and Residual Gas Analysis (RGA). In such cases, the observed process data is composed of a time-series of multi-dimensional sensor readings. The dimensionality of the data involved can be quite large, specially when spectral instruments are used (e.g., OES and RGA which contain 1000 and 100 sensor readings, respectively). Interpretation of these time-series requires highly trained personnel, which has been a deterrent to using such systems in a manufacturing environment.
Handling spectral time-series data is preferably achieved by selecting a few variables a priori and monitoring their behavior as a function of time. This approach has been fully described in an article by S. B. Dolins et al., "Monitoring and Diagnosis of Plasma Etch Process", published in the IEEE Transactions on Semiconductor Manufacturing, Vol. 1, No. 1, February 1988. Statistical models can be built to characterize the temporal behavior of each selected channel. These models can be used to detect unusual conditions by comparing the actual behavior of the process to that predicted by the model and generating alarms based on the various methods (the simplest being using a threshold). There are several disadvantages with this approach:
1. Selecting a good set of variables to monitor is a non-trivial task. Typically, it is based on an ad-hoc selection procedure which may well differ from expert to expert.
2. Models must be built for each individual variable, a time-consuming effort which often leads to systems which only use a small subset of the variables that are measured, resulting in the loss of important information.
3. The selected variables are often cross-correlated, Monitoring such variables individually would be inefficient since the variables convey correlated information. However, monitoring a combined statistic of variables, as fully described by G. E. P. Box et al., in Time Series Analysis Forecasting and Control, published by Holden-Day, 2nd. Edition, San Francisco, 1976, may result in further loss of information.
By way of example, the "cleanliness" of a tool or a process is monitored by measuring the count of foreign material (FM) collected on a wafer passing through the tool. The presence of foreign material is a function of many factors, some of the most common being aging of the process kit components and polymer build-up on the chamber parts. Other methods for checking the performance of a tool include feedback from measurements performed on the product, some of which follow immediately after the wafer exits from the tool. Others may occur days or even weeks later. Whereas these monitoring procedures are usually effective, they do not provide by themselves an ironclad guaranty on the performance of a tool or process required for the manufacture of a product wafer. The aforementioned procedures oftentimes allow a faulty process to occur, which makes it essential to find yet better and more sophisticated approaches to avoid their occurrence. For instance, a small leak or similar disfunction in a constant gas flow may affect the etch profile or its selectivity, and may oftentimes not be detected by normal calibration. In a second example, an incomplete photoresist strip from a previous process step may bring a wafer into a chamber wherein the presence of resist may affect and completely alter the chemical composition of the plasma. The negative implications can be enormous and highly detrimental to the overall process. In yet a third example, an erroneous step may accidentally be used on a product wafer, of which there is little likelihood of recovery. Thus, prior art techniques are inadequate to ensure the proper monitoring and control of processing steps.
Reactive species, e.g., etchants or input gasses commonly used in the process of etching a film are known to be tracked utilizing a variety of techniques. One of the methods most often used tracks the intensity of a wavelength for a particular species using the aforementioned optical emission spectroscopy (OES). An example of how OES is used is described in U.S. Pat. No. 5,288,367 to Augell, and of common assignee, wherein a wavelength of light is used for end-point detection during etching. More particularly, OES is shown to track the amount of volatile etch products or reactive species as a function of film thickness. Spectral data is collected during the process which characterizes variations of light emitted by the discharges produced during etching. At least one principal component of the data is calculated, (hereinafter referred to as the eigenvalue). Each of the eigenvalues has variables, having each a weight. Each variable further corresponds to a given wavelength of the light emitted by the discharge. By examining the weights, it is determined which variables of the eigenvalues varies during the etch process, and the end-point of the etch can be detected by an analysis of the variable or the wavelength.
In U.S. Pat. No. 5,308,414 to O'Neil et al., and of common assignee, shows a method and apparatus for determining the time at which a plasma etching process should be terminated. As in the previously cited reference, the apparatus monitors the optical emission intensity of the plasma in a narrow band around a predetermined spectral band and generates a signal indicative of the spectral intensity of the etch product species. It additionally monitors the OES of the plasma in a wide band, generating another signal indicative of the spectral intensity of the continuum plasma emission. Based on these two signal, it generates a termination signal when the magnitudes diverge.
Prior art optical emission systems are commonly equipped with a photo-array detector having an output that covers the entire visible portion of the spectrum. At a typical sampling rate of 1 second, the data matrix becomes unmanageable in a very short period of time. The sizeable bulk of information is usually highly advantageous because it enables simultaneous observations of various key chemical species of an etch process. Nevertheless, full utilization of the entire data matrix at its high sampling rate cannot be accomplished in a real-time sense. Just to follow the time evolution of peaks and valleys of a spectrum can be instructive to some, but confusing to others. The goal is to utilize the entire spectrum, knowing beforehand that certain peaks are more critical to the etch process than others, and realizing that the intensity of peaks may naturally vary over an extended period of time in the course of an etch process. In practice, what is required in any fabrication line is a single display chart that quantitatively captures the temporal characteristics of multiple peaks and that allows for statistically meaningful control limits, sufficiently simple and easy for an operator to make a judgment on the state of a given process.
Various models can be built through the use of multivariate mathematical techniques to measure the status a plasma process based on OES inputs. These applications provide in real-time simple control charts with 3-sigma limits displaying the results of the model calculations. This process, hereinafter referred to as the "process guard", makes it possible to look in real-time at a plasma using OES. This process guard approach effectively locates faults and accurately pinpoints its source right at the outbreak of the problem. Whereas prior art OES has been mainly used as a "fingerprint" process, experts are routinely called to determine the cause of the problem. The process guard approach detects the presence of problems sufficiently early to minimize scrapping wafers and oftentimes it is instrumental in stopping the process when a serious fault occurs.
Linear algebra computations have been used successfully to separate correlated absorbance signals from spectral noise based on the aforementioned PCA technique, and has been combined with regression techniques to form the Principal Component Regression (PCR) used in conjunction with other statistical methods such as Partial Least Squares (PLS). An example of the use of these and other methods is fully described in U.S. Pat. No. 5,121,337 to Brown. This patent teaches how to correct the measured spectral data so that the data is substantially insensitive to measurement process signals. More particularly, it teaches a method for estimating unknown properties and/or compositional data of a sample which is insensitive to spectral data due to the measurement process itself.
Referring now to FIG. 1, an apparatus described in U.S. Pat. No. 5,288,367 to Angell et al., and of common assignee, and known to the art for monitoring an etching process by selecting a wavelength of light, is shown as representative of the type normally used for implementing the method of monitoring and controlling a plasma process described in the present invention. The apparatus is specifically designed for end-point detection. Spectral data is collected during etching, which characterizes a variation of light emitted by discharge during the process. Eigenvalues of the data is collected, each having variables, each variable having a weight, and each variable further corresponding to a wavelength of the light emitted by the discharge. By examining and analyzing the weights, one may determine which variable of the eigenvalue varies during etching such that the end-point can be detected by monitoring the variable or the corresponding wavelength.
The major components of the RIE system assembly are a cathode 25 inside of a plasma chamber 20 filled with plasma. A gas supply 30 provides the necessary gas to the plasma inside the chamber 20, and a turbo pump 35 evacuates plasma discharge during etching. An RF generator 40 supplies RF power to the cathode 25 to form an RF field in the plasma. An OES 45 is attached to the plasma chamber 20 using port 50 via a fiber optic cable 55 to a photo-diode array detector/DAC (digital to analog converter) 47, referred to as a channel. The array/DAC detects and digitizes the emission signal so that a multi-channel analyzer 49 tracks and records the intensity of each wavelength of the light. Digitized spectral data is sent to a computer 50 for further processing. Additional details of the etching assembly can be obtained from the U.S. Pat. No. 5,288,367, which is herein incorporated by reference.